MultiMind AI bridges the gap between small local models and complex reasoning. It effortlessly auto-discovers endpoints like Ollama and LM Studio (OpenAI-compatible) and lets you deploy three reasoning architectures: a sequential Thinking Pipeline (Planning, Execution, Critique), a parallel Agent Council (Expert Advisors & Lead Judge), or a hierarchical Organisation Mode (CEO → Departments → Employees → Synthesis).

• 🧠 Thinking Pipeline: Elevate smaller models with dedicated Planning, Execution, and Critique phases.
• 🏛 Agent Council: Deploy a committee of expert models in parallel. Several 'Advisors' provide independent perspectives, synthesized by a Lead Judge into a single superior response.
• 🏢 Organisation Mode: Run a hierarchical multi-agent workflow where a CEO decomposes the request, department heads delegate work to specialist roles, and the CEO synthesizes all outputs into one final answer.
• 🔌 Zero-Config Auto-Discovery:
  • Automatically hooks into local Ollama endpoints (http://127.0.0.1:11434).
  • Supports optional discovery for LM Studio (http://127.0.0.1:1234).
• 🎯 Precision Model Mapping: Assign distinct models to handle the different stages of thought or council roles.
• 💬 Immersive UI: Enjoy a streaming timeline interface with collapsible "thought blocks" to keep your UI clean while the AI thinks.
• 📝 Native Markdown & Math Support: Final outputs are rendered as HTML, with math equations typeset using a bundled local KaTeX build.
• ⚡ Frictionless Setup: Purely in-memory settings. Zero .env setup required for your first run.

Get up and running in your local environment in seconds:

# 1. Install the package via pip pip install multimind # 2. Launch the application multimind

# 1. Clone the repository git clone https://github.com/JitseLambrichts/MultiMind-AI.git cd MultiMind-AI # 2. Create a virtual environment python3 -m venv .venv # 3. Activate the virtual environment source .venv/bin/activate # On Windows: .venv\Scripts\activate # 4. Install the package in editable mode pip install -e . # 5. Launch the application multimind

Next: Open your browser and navigate to http://127.0.0.1:8000 🎉

MultiMind AI works seamlessly with standard local APIs:

• Ollama: Connects via /api/chat and /api/tags
• OpenAI-Compatible Servers (e.g., LM Studio): Connects via /v1/chat/completions and /v1/models

If no provider is automatically detected, you can easily point the backend to your local OpenAI-compatible endpoint using the application's settings panel.

The sequential reasoning pipeline elevates the capabilities of standard models by splitting inference into modular steps:

1. Plan: Formulates a detailed technical roadmap to solve the user's request.
2. Execute: Implements the primary solution based on the established plan.
3. Critique (Hard Mode): Rigorously audits the implementation for errors or omissions, delivering a refined, superior final answer.

For complex tasks requiring multiple perspectives, MultiMind AI offers the Agent Council. This architecture emphasizes parallel expertise over sequential steps:

1. Parallel Advisors: Multiple models process the user's request independently, providing diverse expert viewpoints.
2. Diverse Perspectives: Each advisor follows expert-level system prompts to ensure accurate, independent technical analysis.
3. The Judge: A final 'Lead Synthesizer' model reviews all advisor outputs, cross-examines their findings, resolves conflicts, and merges the best elements into a single definitive response.

For tasks that benefit from structured delegation, MultiMind AI includes Organisation Mode. Instead of parallel peers only, this mode simulates an org chart with explicit delegation layers:

1. CEO Planning: A CEO agent analyzes the user request and splits it into department-level sub-tasks.
2. Department Delegation: Each department head converts its sub-task into role-specific assignments.
3. Employee Execution: Specialist employee agents execute their assigned tasks and stream their outputs.
4. CEO Synthesis: The CEO consolidates all department/employee results into a single cohesive final response.

In the UI, this appears as an interactive organisation chart with expandable nodes and streaming outputs per role, while still ending with one final answer block.

⚙️ Configuration: Organisation mode uses one selected model for all agents in the hierarchy (configurable via the Organisation settings panel).

📝 Note: Chat history is intentionally in-memory only for the current MVP.

We evaluated the performance of MultiMind AI's reasoning pipeline using a subset of 20 questions from the GSM8K dataset. The results demonstrate a clear improvement in model accuracy when utilizing the different reasoning modes.